Different from LMNet [1]}, which directly concatenates the range image and residual images together as the input of the original SalsaNext, we use two specific branches to extract appearance features from the range images and motion features from the residual images, respectively.
To preserve descriptive features, we furthermore replace the average pooling in BlockB with SoftPool as suggested by [2]}.
In Enc-A, we place one Meta-Kernel convolution layer after the first ResContextBlock [3]} to learn dynamic weights from relative Cartesian coordinates and enable the network to obtain more geometric information from the range images, making the convolution more suitable to the range images.
Different from the range image branch, in Enc-M, we only use one ResContextBlock to avoid overfitting of the residual images.
